CN109815368A - Resource recommendation method, device, equipment and computer readable storage medium - Google Patents
Resource recommendation method, device, equipment and computer readable storage medium Download PDFInfo
- Publication number
- CN109815368A CN109815368A CN201811502921.2A CN201811502921A CN109815368A CN 109815368 A CN109815368 A CN 109815368A CN 201811502921 A CN201811502921 A CN 201811502921A CN 109815368 A CN109815368 A CN 109815368A
- Authority
- CN
- China
- Prior art keywords
- resource
- user
- content
- classification
- recommendation
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 54
- 238000003860 storage Methods 0.000 title claims abstract description 12
- 230000003993 interaction Effects 0.000 claims description 23
- 230000006399 behavior Effects 0.000 claims description 21
- 238000004590 computer program Methods 0.000 claims description 12
- 238000010586 diagram Methods 0.000 description 6
- 230000006870 function Effects 0.000 description 5
- 230000008569 process Effects 0.000 description 5
- 230000006978 adaptation Effects 0.000 description 3
- 238000004891 communication Methods 0.000 description 3
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 238000012545 processing Methods 0.000 description 3
- 238000012552 review Methods 0.000 description 3
- 238000009826 distribution Methods 0.000 description 2
- 238000004140 cleaning Methods 0.000 description 1
- 238000005520 cutting process Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/43—Processing of content or additional data, e.g. demultiplexing additional data from a digital video stream; Elementary client operations, e.g. monitoring of home network or synchronising decoder's clock; Client middleware
- H04N21/442—Monitoring of processes or resources, e.g. detecting the failure of a recording device, monitoring the downstream bandwidth, the number of times a movie has been viewed, the storage space available from the internal hard disk
- H04N21/44213—Monitoring of end-user related data
- H04N21/44222—Analytics of user selections, e.g. selection of programs or purchase activity
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L15/00—Speech recognition
- G10L15/22—Procedures used during a speech recognition process, e.g. man-machine dialogue
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/41—Structure of client; Structure of client peripherals
- H04N21/422—Input-only peripherals, i.e. input devices connected to specially adapted client devices, e.g. global positioning system [GPS]
- H04N21/42203—Input-only peripherals, i.e. input devices connected to specially adapted client devices, e.g. global positioning system [GPS] sound input device, e.g. microphone
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/43—Processing of content or additional data, e.g. demultiplexing additional data from a digital video stream; Elementary client operations, e.g. monitoring of home network or synchronising decoder's clock; Client middleware
- H04N21/431—Generation of visual interfaces for content selection or interaction; Content or additional data rendering
- H04N21/4312—Generation of visual interfaces for content selection or interaction; Content or additional data rendering involving specific graphical features, e.g. screen layout, special fonts or colors, blinking icons, highlights or animations
- H04N21/4316—Generation of visual interfaces for content selection or interaction; Content or additional data rendering involving specific graphical features, e.g. screen layout, special fonts or colors, blinking icons, highlights or animations for displaying supplemental content in a region of the screen, e.g. an advertisement in a separate window
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/45—Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies, resolving scheduling conflicts
- H04N21/466—Learning process for intelligent management, e.g. learning user preferences for recommending movies
- H04N21/4667—Processing of monitored end-user data, e.g. trend analysis based on the log file of viewer selections
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/45—Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies, resolving scheduling conflicts
- H04N21/466—Learning process for intelligent management, e.g. learning user preferences for recommending movies
- H04N21/4668—Learning process for intelligent management, e.g. learning user preferences for recommending movies for recommending content, e.g. movies
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/47—End-user applications
- H04N21/472—End-user interface for requesting content, additional data or services; End-user interface for interacting with content, e.g. for content reservation or setting reminders, for requesting event notification, for manipulating displayed content
- H04N21/4722—End-user interface for requesting content, additional data or services; End-user interface for interacting with content, e.g. for content reservation or setting reminders, for requesting event notification, for manipulating displayed content for requesting additional data associated with the content
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/47—End-user applications
- H04N21/482—End-user interface for program selection
- H04N21/4826—End-user interface for program selection using recommendation lists, e.g. of programs or channels sorted out according to their score
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L15/00—Speech recognition
- G10L15/22—Procedures used during a speech recognition process, e.g. man-machine dialogue
- G10L2015/223—Execution procedure of a spoken command
Landscapes
- Engineering & Computer Science (AREA)
- Multimedia (AREA)
- Signal Processing (AREA)
- Databases & Information Systems (AREA)
- Health & Medical Sciences (AREA)
- Human Computer Interaction (AREA)
- General Health & Medical Sciences (AREA)
- Social Psychology (AREA)
- Computer Networks & Wireless Communication (AREA)
- Computational Linguistics (AREA)
- Audiology, Speech & Language Pathology (AREA)
- Physics & Mathematics (AREA)
- Acoustics & Sound (AREA)
- Business, Economics & Management (AREA)
- Marketing (AREA)
- Software Systems (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The embodiment of the present invention provides a kind of resource recommendation method, device, equipment and computer readable storage medium.The method of the embodiment of the present invention obtains the recommendation weight of the corresponding recommendable classifying content of the resource class and each classifying content by the resource class according to the first resource provided to the first user；According to the recommendation weight of each classifying content, target classification to be recommended is determined；Recommend the Secondary resource of the target classification to first user, it can be realized and neatly correspond to different recommendable classifying contents for different resource class, and flexibly set the recommendation weight that different resource classification corresponds to recommendable classifying content, so as to according to the resource class for currently providing a user first resource, the Secondary resource for user flexibility recommending different content to classify, preferably meet user demand, improves user experience.
Description
Technical Field
The embodiment of the invention relates to the technical field of communication, in particular to a resource recommendation method, a resource recommendation device, resource recommendation equipment and a computer-readable storage medium.
Background
The current multimedia platform, such as a video playing website, provides a target resource required by a user to the user, and when post recommendation is satisfied, generally recommends the same type of resource with high similarity, high score, or high click rate with the target resource, and the surrounding resources of the target resource to the user. For example, for reasoning about resources such as movies, the playing website may recommend other movies that the starring actor performs to the user, a ranking list of the movies that is ranked by click-through rate or score, and the surrounding resources such as the feature of the movie, the lead actor's interview, etc.
However, after the user has satisfied the target resource, the user does not necessarily want to continue viewing the same type of resource or the peripheral resource having a high similarity to the target resource. For example, after a user finishes watching an inference movie, the most urgent need is to see a detailed movie review or scenario analysis of the inference movie, not other inference movies. The existing resource recommendation method is not flexible enough and cannot meet the requirements of users.
Disclosure of Invention
The embodiment of the invention provides a resource recommendation method, a resource recommendation device and a computer readable storage medium, which are used for solving the problems that the existing resource recommendation method is not flexible enough and cannot meet the requirements of users.
One aspect of the embodiments of the present invention is to provide a resource recommendation method, including:
according to a resource category of a first resource provided for a first user, obtaining recommendable content categories corresponding to the resource category and a recommendation weight of each content category;
determining a target classification to be recommended according to the recommendation weight of each content classification;
recommending the second resource of the target category to the first user.
Another aspect of the embodiments of the present invention is to provide a resource recommendation apparatus, including:
the acquisition module is used for acquiring recommendable content classifications corresponding to resource classifications and recommendation weights of the content classifications according to the resource classifications of first resources provided for a first user;
the determining module is used for determining a target classification to be recommended according to the recommendation weight of each content classification;
and the recommending module is used for recommending the second resource of the target classification to the first user.
Another aspect of the embodiments of the present invention is to provide a resource recommendation device, including:
a memory, a processor, and a computer program stored on the memory and executable on the processor,
the processor implements the resource recommendation method described above when running the computer program.
It is another aspect of an embodiment of the present invention to provide a computer-readable storage medium, storing a computer program,
the computer program, when executed by a processor, implements the resource recommendation method described above.
According to the resource recommendation method, the resource recommendation device, the resource recommendation equipment and the computer readable storage medium, recommendable content classifications corresponding to resource classifications and recommendation weights of the content classifications are obtained according to the resource classification of a first resource provided for a first user; determining a target classification to be recommended according to the recommendation weight of each content classification; the second resource of the target classification is recommended to the first user, different recommendable content classifications can be flexibly corresponding to different resource classifications, and the recommendation weight of the recommendable content classification corresponding to the different resource classifications can be flexibly set, so that the second resource of the different content classifications can be flexibly recommended to the user according to the resource classification of the first resource currently provided to the user, the user requirements can be better met, and the user experience is improved.
Drawings
Fig. 1 is a flowchart of a resource recommendation method according to an embodiment of the present invention;
FIG. 2 is a flowchart of a resource recommendation method according to a second embodiment of the present invention;
FIG. 3 is a diagram illustrating a display area for content classification according to a second embodiment of the present invention;
fig. 4 is a schematic diagram of a voice interaction guidance information display according to a second embodiment of the present invention;
fig. 5 is a schematic structural diagram of a resource recommendation device according to a third embodiment of the present invention;
fig. 6 is a schematic structural diagram of a resource recommendation device according to a fifth embodiment of the present invention.
With the above figures, certain embodiments of the invention have been illustrated and described in more detail below. The drawings and written description are not intended to limit the scope of the inventive concepts in any way, but rather to illustrate the inventive concepts to those skilled in the art by reference to specific embodiments.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with embodiments of the invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of embodiments of the invention, as detailed in the following claims.
The terms "first", "second", etc. referred to in the embodiments of the present invention are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. In the description of the following examples, "plurality" means two or more unless specifically limited otherwise.
The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments. Embodiments of the present invention will be described below with reference to the accompanying drawings.
Example one
Fig. 1 is a flowchart of a resource recommendation method according to an embodiment of the present invention. The embodiment of the invention provides a resource recommendation method aiming at the problem that the existing resource recommendation method is not flexible enough and cannot meet the user requirements.
The method in this embodiment is applied to a computer device of a multimedia platform such as a video playing platform, a music playing platform, a reading platform, and the like, and in other embodiments, the method may also be applied to other devices, and this embodiment takes a terminal device as an example for schematic description.
In this embodiment, the multimedia platform sets corresponding recommendable content classifications for different resource categories and sets different recommendation weights for the recommendable content classifications for the resource categories according to behaviors of a plurality of users after using resources of the resource categories. The larger the recommendation weight for a content category, the more resources representing the user's need for the content category after using the resources.
The application scenario of this embodiment is as follows: the multimedia platform provides a first resource for a first user according to a request of the user, and recommends a plurality of second resources of content classification with maximum recommendation weight to the first user according to recommendable content classification corresponding to the resource classification of the first resource and recommendation weight of each content classification on the basis of meeting the requirement of providing the first resource for the first user, namely providing more needed resources for the user.
As shown in fig. 1, the method comprises the following specific steps:
step S101, obtaining recommendable content classifications corresponding to resource classifications and recommendation weights of each content classification according to the resource classifications of the first resources provided for the first user.
In this embodiment, the first user may be any user currently using the multimedia platform. The first resource is a resource currently provided by the multimedia platform to the first user. The first resource may be a video, music, novel, etc. resource.
The resource categories of the first resource may include a primary category, and a secondary category included in the primary category, and so on. For example, a primary level of resources may be movies, a secondary level may be inference classes, series classes, or the like.
The higher the level of the resource category of the first resource is, the more detailed the classification of the resource category is, the resource category in this embodiment includes several levels and which categories each level includes may be set by a technician according to an actual application scenario and experience, and this embodiment is not specifically limited herein.
In this embodiment, different resource categories may correspond to different recommendable content categories and recommendation weights for the content categories. For each resource category, historical behavior data of users who used resources of the resource category may be obtained, where the historical behavior data includes information of next resources that the users accessed after using the resources of the resource category. According to the historical behavior data, the content classification of the next resource which the user may need to access after using the resource of the resource category can be counted to obtain the content classification to be recommended, and the recommendation weight of the content classification to be recommended can be counted. The larger the number of users whose next resource to access is a certain content category, the larger the recommendation weight of the content category, indicating that the user has more demand for the resource of the content category after using the resource of the resource category.
Optionally, the second resource may be one or a combination of the following information: audio, video, text content, picture set, etc., and the embodiment is not limited in this respect.
Optionally, the multimedia platform may obtain recommendable content classifications corresponding to the resource types and a recommendation weight of each content classification in advance, and store the recommendable content classifications and the recommendation weights locally. In this step, the multimedia platform may locally obtain recommendable content classifications corresponding to resource classifications of the stored first resource and a recommendation weight of each content classification.
For example, the resource category includes at least movies and music, and the recommendable content category corresponding to this resource category of movies may include: film evaluation, plot analysis, highlight cutting, video of a release meeting, promo, video of a related hotspot and the like; recommendable content classifications for this resource category of music may include: song review, lyric analysis, singing audio, recomposing audio, concert version audio, and the like.
Further, the movie is a primary category, and the secondary category of the movie at least includes reasoning and series, and if the resource category of the first resource includes the primary category: movies, and secondary categories: reasoning; the recommendation weight of the scenario analysis in the recommendable content classification may be configured to be higher, preferentially recommending the scenario analysis to the user. If the resource type of the first resource includes a primary type: movies, and secondary categories: series; the recommendation weight of the highlight clips in the recommendable content category may be configured to be higher, with preference to recommend the highlight clips to the user, such as "hero life highlight clips" or the like.
And S102, determining a target classification to be recommended according to the recommendation weight of each content classification.
In this step, according to recommendable content classifications corresponding to the resource categories of the first resource and the recommendation weight of each content classification, a plurality of content classifications with the highest recommendation weight are determined as target classifications recommended to the first user at this time.
And step S103, recommending the second resource of the target classification to the first user.
After determining the target classification to be recommended, in the step, the multimedia platform acquires a second resource which is related to the first resource and belongs to the target classification according to the attribute information of the first resource; and recommending the entry information of the second resource belonging to the target classification to the first user so that the user can conveniently acquire the second resource through the entry information.
The second resource may be a resource available by the multimedia platform itself, or may be a resource of another platform, which is not specifically limited herein.
The attribute information of the first resource may include a resource name, a resource category, an author, and other information of the first resource, and the like, and this embodiment is not limited in this embodiment. The other resources related to the first resource may be other resources that the author of the first resource participates in authoring, comment information, content analysis information, preview information, or promotion information of the first resource, etc., secondary adaptation of the first resource, or resources of different versions, etc., and other related resources, etc., and this embodiment is not limited in particular here.
Optionally, when recommending the second resource of the target classification to the first user, if the second resource includes the hot spot resource of the first resource, preferentially recommending the hot spot resource of the first resource to the first user. The hot spot resource refers to a resource related to a hot spot event occurring within a preset time period. For example, the group of dream scenarios of Red mansions meets the video resources of a certain comprehensive program in decades. The preset time period may be set by a technician according to an actual application scenario and experience, and this embodiment is not specifically limited herein.
According to the resource category of the first resource provided for the first user, recommendable content categories corresponding to the resource category and recommendation weight of each content category are obtained; determining a target classification to be recommended according to the recommendation weight of each content classification; the second resource of the target classification is recommended to the first user, different recommendable content classifications can be flexibly corresponding to different resource classifications, the recommendation weight of the recommendable content classifications corresponding to the different resource classifications can be flexibly set, and therefore the second resource of the different content classifications can be flexibly recommended to the user according to the resource classification of the first resource currently provided to the user, user requirements are better met, and user experience is improved.
Example two
Fig. 2 is a flowchart of a resource recommendation method according to a second embodiment of the present invention. On the basis of the first embodiment, in this embodiment, specifically, the recommending the second resource of the target classification to the first user may include the following two implementation manners: one is, according to the attribute information of the first resource, obtain the second resource of the target classification related to first resource; and classifying and displaying the entrance information of the second resource in the display interface of the first resource. The other is that voice interaction guiding information is displayed on a display interface of the first resource, and the voice interaction guiding information comprises a target classification; receiving voice information input by a user; performing semantic analysis on voice information input by a user, and determining a target classification selected by the user; and opening the second resource of the target classification selected by the user.
As shown in fig. 2, the method comprises the following specific steps:
step S201, obtaining recommendable content categories corresponding to resource categories and recommendation weights of each content category according to the resource categories of the first resource provided to the first user.
In this embodiment, the first user may be any user currently using the multimedia platform. The first resource is a resource currently provided by the multimedia platform to the first user. The first resource may be a video, music, novel, etc. resource.
The resource categories of the first resource may include a primary category, and a secondary category included in the primary category, and so on. For example, a primary level of resources may be movies, a secondary level may be inference classes, series classes, or the like.
The higher the level of the resource category of the first resource is, the more detailed the classification of the resource category is, the resource category in this embodiment includes several levels and which categories each level includes may be set by a technician according to an actual application scenario and experience, and this embodiment is not specifically limited herein.
In this embodiment, different resource categories may correspond to different recommendable content categories and recommendation weights for the content categories. For each resource category, historical behavior data of users who used resources of the resource category may be obtained, where the historical behavior data includes information of next resources that the users accessed after using the resources of the resource category. According to the historical behavior data, the content classification of the next resource which the user may need to access after using the resource of the resource category can be counted to obtain the content classification to be recommended, and the recommendation weight of the content classification to be recommended can be counted. The larger the number of users whose next resource to access is a certain content category, the larger the recommendation weight of the content category, indicating that the user has more demand for the resource of the content category after using the resource of the resource category.
Optionally, the multimedia platform may obtain recommendable content classifications corresponding to the resource types and a recommendation weight of each content classification in advance, and store the recommendable content classifications and the recommendation weights locally. In this step, the multimedia platform may locally obtain recommendable content classifications corresponding to resource classifications of the stored first resource and a recommendation weight of each content classification.
Step S202, determining a target classification to be recommended according to the recommendation weight of each content classification.
In this step, according to recommendable content classifications corresponding to the resource categories of the first resource and the recommendation weight of each content classification, a plurality of content classifications with the highest recommendation weight are determined as target classifications recommended to the first user at this time.
In this embodiment, the step may be specifically implemented as follows:
acquiring historical behavior data of a second user using resources of the resource type according to the resource type of a first resource provided for the first user; according to the historical behavior data of the second user, determining the content classification selected by the second user after the second user uses the resource of the resource type each time, and obtaining the recommendable content classification corresponding to the resource type; and determining the recommendation weight of each content classification according to the times of selecting each content classification by the second user.
Further, according to the recommendation weight of each content classification, the target classification to be recommended is determined, and one mode that can be adopted is as follows:
and determining the content classification with the recommendation weight larger than the weight threshold as a target classification to be recommended according to the recommendation weight of each content classification. The weight threshold may be set by a technician according to experience, and this embodiment is not limited in detail here.
According to the recommendation weight of each content classification, determining a target classification to be recommended, wherein another mode can be adopted as follows:
and determining the preset number of content classes with the maximum recommendation weight as target classes to be recommended according to the recommendation weight of each content class. The preset number may be set by a technician according to experience, and the embodiment is not specifically limited herein.
Optionally, the step may be specifically implemented by the following method:
acquiring historical behavior data of a first user according to a resource type of a first resource provided for the first user; determining the content classification of the next resource selected by the first user after using the resource of the resource category each time according to the historical behavior data of the first user to obtain the recommendable content classification corresponding to the resource category; and determining the recommendation weight of each content classification according to the times of selecting each content classification by the first user, so that personalized recommendation can be performed for the first user.
Optionally, the step may be specifically implemented by the following method:
acquiring historical access data of a first resource according to the resource category of the first resource provided for a first user; determining the content classification of the next resource selected by the user after using the first resource each time according to the historical access data of the first resource to obtain a recommendable content classification corresponding to the resource classification of the first resource; and determining the recommendation weight of each content classification according to the times of selecting each content classification by the first user, so that the first resource can be recommended in a targeted manner.
The acquired historical behavior data and historical access data may include behavior data or access data that is not actively operated by the user, for example, automatically playing to the next video, etc. Optionally, in this embodiment, the historical behavior data and the historical access data may be subjected to data cleaning, and behavior data or access data that is not actively operated by the user is removed, so that the recommendation weight of each content category is closer to the subjective needs of the user.
Optionally, determining a target category to be recommended according to the recommendation weight of each content category, which may further include:
determining whether the first resource is online or not according to the online time of the first resource; if the first resource is not on line and the forecast class is not included in the target classification, determining the recommendation weight of the forecast class according to the maximum value of the recommendation weight of the target classification, so that the forecast class is the content classification with the maximum recommendation weight in the target classification of the first resource; and taking the forecast class as a target class to be recommended. If the first resource is online, no special processing is performed.
For example, when a movie is just shown and no online resource is currently available, a trailer is recommended preferentially.
Step S203, according to the attribute information of the first resource, acquiring a second resource of the target classification related to the first resource.
After determining the target classification to be recommended, in the step, the multimedia platform acquires a second resource which is related to the first resource and belongs to the target classification according to the attribute information of the first resource; and recommending the entry information of the second resource belonging to the target classification to the first user so that the user can conveniently acquire the second resource through the entry information.
The second resource may be a resource available by the multimedia platform itself, or may be a resource of another platform, which is not specifically limited herein.
The attribute information of the first resource may include a resource name, a resource category, an author, and other information of the first resource, and the like, and this embodiment is not limited in this embodiment. The other resources related to the first resource may be other resources that the author of the first resource participates in authoring, comment information, content analysis information, preview information, or promotion information of the first resource, etc., secondary adaptation of the first resource, or resources of different versions, etc., and other related resources, etc., and this embodiment is not limited in particular here.
And step S204, classifying and displaying the entrance information of the second resource in the display interface of the first resource.
Specifically, the display of the eye-catching entrance information is added in the display interface of the first resource.
Optionally, the entry information of the second resource is displayed in a classified manner in the display interface of the first resource, and the following manner may be adopted:
determining a display area of the content classification of the second resource in the display interface according to the recommendation weight of the content classification of the second resource, wherein the larger the recommendation weight, the more the display area of the content classification is; and displaying the entrance information of the second resource with different content classifications in the corresponding display area.
In this embodiment, the sizes of the display areas corresponding to the content classifications may be different, and the number of the entry information of the second resource displayed in the display area corresponding to each content classification may also be different.
For example, in fig. 3, a display mode in which entry information of the second resource is displayed in a classified manner in the display interface of the first resource is exemplified by a display area in which four contents of "drama analysis", "highlight", and "actor's visit" are displayed in a classified manner. In addition, in fig. 3, only the display area corresponding to the content classification is set on the right side of the playback area as an example, the display area of the content classification may be set in another area except the playback area of the display interface, and the embodiment is not limited in detail here.
Optionally, the entry information of the second resource is displayed in a classified manner in the display interface of the first resource, and the following manner may also be adopted:
adding a content classification identifier in the entry information of the second resource according to the content classification of the second resource; and displaying the entrance information of the second resource in the display interface of the first resource.
For example, a corner mark identifier corresponding to the content classification may be added to the thumbnail displayed in the entry information of the second resource, and the corner mark identifiers of different content classifications are different.
For example, a content classification universal symbol similar to "[ may also be added to the original title in the entry information of the second resource, and the universal symbol classifies the content of the resource, for example, the title of the second resource displayed may be similar to the following manner: "[ content class ] -original title"; for example, the "specialization" was created as a talk show by the chief of xxx and the "foresight" was created as a foresight in the first edition of xxx.
Optionally, the entry information of the second resource may be displayed at a preset eye-catching position in a dynamic manner such as eye-catching color, font, picture, or through a floating window.
Optionally, the entry information of the second resource is displayed in a primary entry manner. Wherein, the first-level entry is entry information which can jump to the second resource through one selection operation.
In addition, the display area of each content category is sorted according to the relevance, click rate, score and the like of the first resource, and then displayed in the display area corresponding to the category content. Further, according to the size of the display area corresponding to the classified content, displaying the entry information of a plurality of second resources ranked most front; or, the entry information of a certain number of second resources is specified in the display area corresponding to the classified content, and a more option is displayed, so that the user can conveniently click the more option to view the entry information of more second resources in the content classification. The specified number may be set by a technician according to the layout of the display interface, and this embodiment is not specifically limited herein.
The above steps S203-S204 are a process of recommending the second resource of the target category to the first user by adding the eye-catcher.
Step S205, displaying voice interaction guiding information on a display interface of the first resource, wherein the voice interaction guiding information comprises a target classification.
In this embodiment, for a platform with an intelligent voice interaction function, such as a smart speaker, etc., a second resource of the target classification may also be recommended to the first user in a voice interaction manner.
Specifically, the voice interaction guidance information may be displayed on the display interface of the first resource, and the voice interaction guidance information may be displayed below the playing area or at another highlighted position, so that the user may easily find the voice interaction guidance information and select the target category recommended to the user in a voice manner according to the voice interaction guidance information.
For example, as shown in fig. 4, the following voice interaction guidance information may be displayed below the play area: "you can say 'i want to see movie reviews', 'i want to see clips' to me".
Optionally, when the first resource is played to enter the tail, the voice interaction guidance information may be started to be displayed, so as to avoid affecting the use of the first resource by the user.
And step S206, receiving the voice information input by the user.
And receiving voice information input by the user in real time so as to provide the second resource to the user in time.
Step S207, performing semantic analysis on the voice information input by the user, and determining the target classification selected by the user.
After receiving voice information input by a user, performing semantic analysis on the voice information to extract a target classification included in the voice information.
And step S208, opening the second resource of the target classification selected by the user.
According to the target classification selected by the user, the second resource of the target classification selected by the user can be directly opened. If the target classification second resources selected by the user are multiple, the second resources with the highest scores or click rates can be opened according to the scores or click rates of the multiple second resources and the first resources.
The steps S205-S208 are processes of recommending the second resource of the target classification to the first user by means of voice interaction.
In this embodiment, the above steps S203 to S204 and steps S205 to S208 are two possible implementations of recommending the second resource of the target category to the first user, respectively, and the recommending the second resource of the target category to the first user may be implemented in any one of the two implementations, or in two implementations by the user at the same time.
In addition, in this embodiment, when recommending the second resource of the target classification to the first user, if the second resource includes the hot resource of the first resource, the hot resource of the first resource is preferentially recommended to the first user. The hot spot resource refers to a resource related to a hot spot event occurring within a preset time period. For example, the group of dream scenarios of Red mansions meets the video resources of a certain comprehensive program in decades. The preset time period may be set by a technician according to an actual application scenario and experience, and this embodiment is not specifically limited herein.
The method provided by the embodiment can meet the next step requirement of the user based on the scene, reduce the input operation cost of the user and improve the user experience. Meanwhile, the multimedia platform is provided with a new flow distribution inlet, flow backflow can be carried out on target resources, and further the commercial value of the multimedia platform is increased.
The embodiment of the invention can flexibly correspond different recommendable content classifications to different resource classes and flexibly set the recommendation weights of the recommendable content classifications corresponding to the different resource classes, thereby flexibly recommending second resources of different content classifications to the user according to the resource class of the first resource currently provided for the user, displaying the second resources of the content classification with high recommendation weight in the front display area when recommending the second resources to the user, and/or performing classification display of the second resources in a mode of adding content classification identifiers to the entrance information, so that the user can conveniently obtain the required second resources. In addition, a second resource is provided for the user in a voice interaction mode, so that the method is more convenient and faster, and the user experience is improved.
EXAMPLE III
Fig. 5 is a schematic structural diagram of a resource recommendation device according to a third embodiment of the present invention. The resource recommendation device provided by the embodiment of the invention can execute the processing flow provided by the resource recommendation method. As shown in fig. 5, the resource recommendation device 30 includes: an acquisition module 301, a determination module 302 and a recommendation module 303.
Specifically, the obtaining module 301 is configured to obtain recommendable content classifications corresponding to resource classifications and a recommendation weight of each content classification according to the resource classifications of the first resource provided to the first user.
The determining module 302 is configured to determine a target category to be recommended according to the recommendation weight of each content category.
The recommending module 303 is configured to recommend the second resource of the target category to the first user.
The apparatus provided in the embodiment of the present invention may be specifically configured to execute the method embodiment provided in the first embodiment, and specific functions are not described herein again.
According to the resource category of the first resource provided for the first user, recommendable content categories corresponding to the resource category and recommendation weight of each content category are obtained; determining a target classification to be recommended according to the recommendation weight of each content classification; the second resource of the target classification is recommended to the first user, different recommendable content classifications can be flexibly corresponding to different resource classifications, the recommendation weight of the recommendable content classifications corresponding to the different resource classifications can be flexibly set, and therefore the second resource of the different content classifications can be flexibly recommended to the user according to the resource classification of the first resource currently provided to the user, user requirements are better met, and user experience is improved.
Example four
On the basis of the third embodiment, in this embodiment, the obtaining module is further configured to:
acquiring historical behavior data of a second user using resources of the resource type according to the resource type of a first resource provided for the first user; according to the historical behavior data of the second user, determining the content classification selected by the second user after the second user uses the resource of the resource type each time, and obtaining the recommendable content classification corresponding to the resource type; and determining the recommendation weight of each content classification according to the times of selecting each content classification by the second user.
Optionally, the obtaining module is further configured to:
and determining the content classification with the recommendation weight larger than the weight threshold as a target classification to be recommended according to the recommendation weight of each content classification.
Optionally, the obtaining module is further configured to:
and determining the preset number of content classes with the maximum recommendation weight as target classes to be recommended according to the recommendation weight of each content class.
Optionally, the determining module is further configured to:
determining whether the first resource is online or not according to the online time of the first resource; if the first resource is not on line and the forecast category is not included in the target category, determining the recommendation weight of the forecast category according to the maximum value of the recommendation weight of the target category; and taking the forecast class as a target class to be recommended.
Optionally, the recommending module is further configured to:
acquiring a second resource of a target classification related to the first resource according to the attribute information of the first resource; and classifying and displaying the entrance information of the second resource in the display interface of the first resource.
Optionally, the recommending module is further configured to:
determining a display area of the content classification of the second resource in the display interface according to the recommendation weight of the content classification of the second resource, wherein the larger the recommendation weight, the more the display area of the content classification is; and displaying the entrance information of the second resource with different content classifications in the corresponding display area.
Optionally, the recommending module is further configured to:
adding a content classification identifier in the entry information of the second resource according to the content classification of the second resource; and displaying the entrance information of the second resource in the display interface of the first resource.
Optionally, the recommending module is further configured to:
displaying voice interaction guide information on a display interface of the first resource, wherein the voice interaction guide information comprises a target classification; receiving voice information of a user input voice; performing semantic analysis on voice information input by a user, and determining a target classification selected by the user; and opening the second resource of the target classification selected by the user.
The apparatus provided in the embodiment of the present invention may be specifically configured to execute the method embodiment provided in the second embodiment, and specific functions are not described herein again.
The embodiment of the invention can flexibly correspond different recommendable content classifications to different resource classes and flexibly set the recommendation weights of the recommendable content classifications corresponding to the different resource classes, thereby flexibly recommending second resources of different content classifications to the user according to the resource class of the first resource currently provided for the user, displaying the second resources of the content classification with high recommendation weight in the front display area when recommending the second resources to the user, and/or performing classification display of the second resources in a mode of adding content classification identifiers to the entrance information, so that the user can conveniently obtain the required second resources. In addition, a second resource is provided for the user in a voice interaction mode, so that the method is more convenient and faster, and the user experience is improved.
EXAMPLE five
Fig. 6 is a schematic structural diagram of a resource recommendation device according to a fifth embodiment of the present invention. As shown in fig. 6, the resource recommendation device 50 includes: a processor 501, a memory 502, and computer programs stored on the memory 502 and executable by the processor 501.
The processor 501, when executing the computer program stored on the memory 502, implements the resource recommendation method provided by any of the method embodiments described above.
According to the resource category of the first resource provided for the first user, recommendable content categories corresponding to the resource category and recommendation weight of each content category are obtained; determining a target classification to be recommended according to the recommendation weight of each content classification; the second resource of the target classification is recommended to the first user, different recommendable content classifications can be flexibly corresponding to different resource classifications, the recommendation weight of the recommendable content classifications corresponding to the different resource classifications can be flexibly set, and therefore the second resource of the different content classifications can be flexibly recommended to the user according to the resource classification of the first resource currently provided to the user, user requirements are better met, and user experience is improved.
In addition, an embodiment of the present invention further provides a computer-readable storage medium, in which a computer program is stored, and when the computer program is executed by a processor, the resource recommendation method provided in any of the above method embodiments is implemented.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
The integrated unit implemented in the form of a software functional unit may be stored in a computer readable storage medium. The software functional unit is stored in a storage medium and includes several instructions to enable a computer device (which may be a personal computer, a server, or a network device) or a processor (processor) to execute some steps of the methods according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
It is obvious to those skilled in the art that, for convenience and simplicity of description, the foregoing division of the functional modules is merely used as an example, and in practical applications, the above function distribution may be performed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to perform all or part of the above described functions. For the specific working process of the device described above, reference may be made to the corresponding process in the foregoing method embodiment, which is not described herein again.
Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.
It will be understood that the invention is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the invention is limited only by the appended claims.
Claims (18)
1. A resource recommendation method, comprising:
according to a resource category of a first resource provided for a first user, obtaining recommendable content categories corresponding to the resource category and a recommendation weight of each content category;
determining a target classification to be recommended according to the recommendation weight of each content classification;
recommending the second resource of the target category to the first user.
2. The method of claim 1, wherein obtaining recommendable content categories corresponding to resource categories and recommendation weights for each of the content categories according to the resource categories of the first resource provided to the first user comprises:
acquiring historical behavior data of a second user using resources of the resource type according to the resource type of a first resource provided for a first user;
according to the historical behavior data of the second user, determining the content classification selected by the second user after the second user uses the resource of the resource category each time, and obtaining recommendable content classification corresponding to the resource category;
and determining the recommendation weight of each content classification according to the times of selecting each content classification by the second user.
3. The method according to claim 1, wherein the determining a target classification to be recommended according to the recommendation weight of each content classification comprises:
according to the recommendation weight of each content classification, determining the content classification with the recommendation weight larger than a weight threshold as a target classification to be recommended;
or,
and determining the preset number of content classes with the maximum recommendation weight as target classes to be recommended according to the recommendation weight of each content class.
4. The method according to claim 3, wherein the determining a target classification to be recommended according to the recommendation weight of each of the content classifications further comprises:
determining whether the first resource is on-line or not according to the on-line time of the first resource;
if the first resource is not on line and the forecast category is not included in the target category, determining the recommendation weight of the forecast category according to the maximum value of the recommendation weight of the target category;
and taking the forecast class as a target class to be recommended.
5. The method of claim 1, wherein recommending the second resource of the target category to the first user comprises:
acquiring a second resource of the target classification related to the first resource according to the attribute information of the first resource;
and classifying and displaying the entrance information of the second resource in a display interface of the first resource.
6. The method according to claim 5, wherein the classifying and displaying the entry information of the second resource in the display interface of the first resource comprises:
determining a display area of the content classification of the second resource in the display interface according to the recommendation weight of the content classification of the second resource, wherein the larger the recommendation weight, the more the display area of the content classification is;
and displaying the entrance information of the second resource with different content classifications in the corresponding display area.
7. The method according to claim 5, wherein the classifying and displaying the entry information of the second resource in the display interface of the first resource according to the content classification of the second resource comprises:
adding a content classification identifier in the entry information of the second resource according to the content classification of the second resource;
and displaying the entrance information of the second resource in the display interface of the first resource.
8. The method of claim 1 or 5, wherein recommending the second resource of the target category to the first user comprises:
and displaying voice interaction guiding information on a display interface of the first resource, wherein the voice interaction guiding information comprises the target classification.
9. The method of claim 8, wherein after displaying the voice interaction guidance information on the display interface of the first resource, further comprising:
receiving voice information input by a user;
performing semantic analysis on the voice information input by the user to determine a target classification selected by the user;
and opening the second resource of the target classification selected by the user.
10. A resource recommendation device, comprising:
the acquisition module is used for acquiring recommendable content classifications corresponding to resource classifications and recommendation weights of the content classifications according to the resource classifications of first resources provided for a first user;
the determining module is used for determining a target classification to be recommended according to the recommendation weight of each content classification;
and the recommending module is used for recommending the second resource of the target classification to the first user.
11. The apparatus of claim 10, wherein the obtaining module is further configured to:
acquiring historical behavior data of a second user using resources of the resource type according to the resource type of a first resource provided for a first user;
according to the historical behavior data of the second user, determining the content classification selected by the second user after the second user uses the resource of the resource category each time, and obtaining recommendable content classification corresponding to the resource category;
and determining the recommendation weight of each content classification according to the times of selecting each content classification by the second user.
12. The apparatus of claim 11, wherein the determining module is further configured to:
determining whether the first resource is on-line or not according to the on-line time of the first resource;
if the first resource is not on line and the forecast category is not included in the target category, determining the recommendation weight of the forecast category according to the maximum value of the recommendation weight of the target category;
and taking the forecast class as a target class to be recommended.
13. The apparatus of claim 10, wherein the recommendation module is further configured to:
acquiring a second resource of the target classification related to the first resource according to the attribute information of the first resource;
and classifying and displaying the entrance information of the second resource in a display interface of the first resource.
14. The apparatus of claim 13, wherein the recommendation module is further configured to:
determining a display area of the content classification of the second resource in the display interface according to the recommendation weight of the content classification of the second resource, wherein the larger the recommendation weight, the more the display area of the content classification is;
and displaying the entrance information of the second resource with different content classifications in the corresponding display area.
15. The apparatus of claim 10 or 13, wherein the recommendation module is further configured to:
and displaying voice interaction guiding information on a display interface of the first resource, wherein the voice interaction guiding information comprises the target classification.
16. The apparatus of claim 15, wherein the recommendation module is further configured to:
receiving voice information input by a user;
performing semantic analysis on the voice information input by the user to determine a target classification selected by the user;
and opening the second resource of the target classification selected by the user.
17. A resource recommendation device, comprising:
a memory, a processor, and a computer program stored on the memory and executable on the processor,
the processor, when executing the computer program, implements the method of any of claims 1-9.
18. A computer-readable storage medium, in which a computer program is stored,
the computer program, when executed by a processor, implementing the method of any one of claims 1-9.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811502921.2A CN109815368A (en) | 2018-12-10 | 2018-12-10 | Resource recommendation method, device, equipment and computer readable storage medium |
| US16/563,225 US11153653B2 (en) | 2018-12-10 | 2019-09-06 | Resource recommendation method, device, apparatus and computer readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811502921.2A CN109815368A (en) | 2018-12-10 | 2018-12-10 | Resource recommendation method, device, equipment and computer readable storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109815368A true CN109815368A (en) | 2019-05-28 |
Family
ID=66601951
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811502921.2A Pending CN109815368A (en) | 2018-12-10 | 2018-12-10 | Resource recommendation method, device, equipment and computer readable storage medium |
Country Status (2)
| Country | Link |
|---|---|
| US (1) | US11153653B2 (en) |
| CN (1) | CN109815368A (en) |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110333840A (en) * | 2019-06-28 | 2019-10-15 | 百度在线网络技术（北京）有限公司 | Recommended method, device, electronic equipment and storage medium |
| CN110347781A (en) * | 2019-07-18 | 2019-10-18 | 腾讯科技（深圳）有限公司 | Article falls discharge method, article recommended method, device, equipment and storage medium |
| CN110717337A (en) * | 2019-09-29 | 2020-01-21 | 北京声智科技有限公司 | Information processing method, device, computing equipment and storage medium |
| CN111008335A (en) * | 2019-12-20 | 2020-04-14 | 腾讯科技（深圳）有限公司 | Information processing method, device, equipment and storage medium |
| CN112000820A (en) * | 2020-08-10 | 2020-11-27 | 海信电子科技(武汉)有限公司 | Media asset recommendation method and display device |
| CN112099912A (en) * | 2020-08-31 | 2020-12-18 | 安徽永旋通讯科技有限公司 | Computer system integration platform |
| CN111259259B (en) * | 2020-03-11 | 2021-03-30 | 郑州工程技术学院 | University student news recommendation method, device, equipment and storage medium |
| CN113111197A (en) * | 2021-04-16 | 2021-07-13 | 百度在线网络技术（北京）有限公司 | Multimedia content recommendation method, device, equipment and storage medium |
| CN113296652A (en) * | 2021-06-21 | 2021-08-24 | 北京有竹居网络技术有限公司 | Control method and device of electronic equipment, terminal and storage medium |
| CN113379514A (en) * | 2021-01-28 | 2021-09-10 | 北京沃东天骏信息技术有限公司 | Information recommendation method and device, electronic equipment and medium |
| CN113918737A (en) * | 2021-11-18 | 2022-01-11 | 北京达佳互联信息技术有限公司 | Method and device for collecting multimedia resources |
| WO2022062507A1 (en) * | 2020-09-23 | 2022-03-31 | 北京沃东天骏信息技术有限公司 | Information recommendation method and device |
Families Citing this family (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110598086B (en) * | 2018-05-25 | 2020-11-24 | 腾讯科技（深圳）有限公司 | Article recommendation method, device, computer equipment and storage medium |
| CN111400615B (en) * | 2020-03-19 | 2023-03-24 | 腾讯科技（深圳）有限公司 | Resource recommendation method, device, equipment and storage medium |
| CN111428134B (en) * | 2020-03-19 | 2023-07-14 | 北京乐蜜科技有限责任公司 | Recommendation information acquisition method and device, electronic equipment and storage medium |
| CN112231580B (en) * | 2020-11-10 | 2024-04-02 | 腾讯科技（深圳）有限公司 | Information recommendation method and device based on artificial intelligence, electronic equipment and storage medium |
| CN112559859B (en) * | 2020-12-08 | 2023-06-30 | 杭州海康威视系统技术有限公司 | Resource recommendation method, device, electronic device, and machine-readable storage medium |
| US11523176B1 (en) | 2021-06-15 | 2022-12-06 | Rovi Guides, Inc. | System and method to identify and recommend media consumption options based on viewer suggestions |
| US11917227B2 (en) * | 2021-06-15 | 2024-02-27 | Rovi Guides, Inc. | System and method to identify and recommend media consumption options based on viewer suggestions |
| CN113849688B (en) * | 2021-09-18 | 2025-09-30 | 北京百度网讯科技有限公司 | Resource processing method, resource processing device, electronic device and storage medium |
| CN114117223B (en) * | 2021-11-29 | 2025-08-19 | 泰康保险集团股份有限公司 | Insurance recommendation method, apparatus, equipment and medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104376021A (en) * | 2013-08-16 | 2015-02-25 | 捷达世软件（深圳）有限公司 | File recommending system and method |
| CN104391999A (en) * | 2014-12-15 | 2015-03-04 | 北京国双科技有限公司 | Information recommendation method and device |
| CN107818166A (en) * | 2017-11-07 | 2018-03-20 | 暴风集团股份有限公司 | A kind of information recommends method, apparatus, server and system |
| CN108628886A (en) * | 2017-03-20 | 2018-10-09 | 中移（杭州）信息技术有限公司 | A kind of audio file recommendation method and device |
| CN108769725A (en) * | 2018-06-07 | 2018-11-06 | 广州虎牙信息科技有限公司 | Application program launching method, device, storage medium and computer equipment is broadcast live |
Family Cites Families (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6438752B1 (en) * | 1999-06-22 | 2002-08-20 | Mediaone Group, Inc. | Method and system for selecting television programs based on the past selection history of an identified user |
| JP4779481B2 (en) * | 2005-07-19 | 2011-09-28 | ソニー株式会社 | Information processing apparatus and method, and program |
| US7970922B2 (en) * | 2006-07-11 | 2011-06-28 | Napo Enterprises, Llc | P2P real time media recommendations |
| KR100821623B1 (en) * | 2006-07-19 | 2008-04-15 | 삼성전자주식회사 | A computer-readable recording medium for recording a program for recommending a program, an apparatus and a program for executing the method on a computer |
| KR20100100180A (en) * | 2009-03-05 | 2010-09-15 | 삼성전자주식회사 | A method and apparatus for recommending contents |
| US9075875B1 (en) * | 2011-05-13 | 2015-07-07 | Google Inc. | System and method for recommending television programs based on user search queries |
| US9230212B2 (en) * | 2012-02-02 | 2016-01-05 | Peel Technologies, Inc. | Content based recommendation system |
| KR20150066193A (en) * | 2013-12-06 | 2015-06-16 | 삼성전자주식회사 | Display apparatus, display system and serch result providing methods thereof |
| US20160078489A1 (en) * | 2014-09-15 | 2016-03-17 | Verizon Patent And Licensing Inc. | Systems and Methods of Using Social Media Data to Personalize Media Content Recommendations |
| US10631057B2 (en) * | 2015-07-24 | 2020-04-21 | Nuance Communications, Inc. | System and method for natural language driven search and discovery in large data sources |
| US10356456B2 (en) * | 2015-11-05 | 2019-07-16 | Adobe Inc. | Generating customized video previews |
| US10341728B2 (en) * | 2015-12-30 | 2019-07-02 | Sling Media L.L.C. | Media systems for temporally and contextually relevant recommendations |
-
2018
- 2018-12-10 CN CN201811502921.2A patent/CN109815368A/en active Pending
-
2019
- 2019-09-06 US US16/563,225 patent/US11153653B2/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104376021A (en) * | 2013-08-16 | 2015-02-25 | 捷达世软件（深圳）有限公司 | File recommending system and method |
| CN104391999A (en) * | 2014-12-15 | 2015-03-04 | 北京国双科技有限公司 | Information recommendation method and device |
| CN108628886A (en) * | 2017-03-20 | 2018-10-09 | 中移（杭州）信息技术有限公司 | A kind of audio file recommendation method and device |
| CN107818166A (en) * | 2017-11-07 | 2018-03-20 | 暴风集团股份有限公司 | A kind of information recommends method, apparatus, server and system |
| CN108769725A (en) * | 2018-06-07 | 2018-11-06 | 广州虎牙信息科技有限公司 | Application program launching method, device, storage medium and computer equipment is broadcast live |
Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110333840A (en) * | 2019-06-28 | 2019-10-15 | 百度在线网络技术（北京）有限公司 | Recommended method, device, electronic equipment and storage medium |
| CN110333840B (en) * | 2019-06-28 | 2023-04-18 | 百度在线网络技术（北京）有限公司 | Recommendation method and device, electronic equipment and storage medium |
| CN110347781A (en) * | 2019-07-18 | 2019-10-18 | 腾讯科技（深圳）有限公司 | Article falls discharge method, article recommended method, device, equipment and storage medium |
| CN110347781B (en) * | 2019-07-18 | 2023-10-20 | 深圳市雅阅科技有限公司 | Article reverse arrangement method, article recommendation method, device, equipment and storage medium |
| CN110717337A (en) * | 2019-09-29 | 2020-01-21 | 北京声智科技有限公司 | Information processing method, device, computing equipment and storage medium |
| CN111008335A (en) * | 2019-12-20 | 2020-04-14 | 腾讯科技（深圳）有限公司 | Information processing method, device, equipment and storage medium |
| CN111259259B (en) * | 2020-03-11 | 2021-03-30 | 郑州工程技术学院 | University student news recommendation method, device, equipment and storage medium |
| CN112000820A (en) * | 2020-08-10 | 2020-11-27 | 海信电子科技(武汉)有限公司 | Media asset recommendation method and display device |
| CN112099912A (en) * | 2020-08-31 | 2020-12-18 | 安徽永旋通讯科技有限公司 | Computer system integration platform |
| WO2022062507A1 (en) * | 2020-09-23 | 2022-03-31 | 北京沃东天骏信息技术有限公司 | Information recommendation method and device |
| EP4220526A4 (en) * | 2020-09-23 | 2024-01-24 | Beijing Wodong Tianjun Information Technology Co., Ltd. | Information recommendation method and device |
| CN113379514A (en) * | 2021-01-28 | 2021-09-10 | 北京沃东天骏信息技术有限公司 | Information recommendation method and device, electronic equipment and medium |
| CN113111197A (en) * | 2021-04-16 | 2021-07-13 | 百度在线网络技术（北京）有限公司 | Multimedia content recommendation method, device, equipment and storage medium |
| CN113296652A (en) * | 2021-06-21 | 2021-08-24 | 北京有竹居网络技术有限公司 | Control method and device of electronic equipment, terminal and storage medium |
| CN113918737A (en) * | 2021-11-18 | 2022-01-11 | 北京达佳互联信息技术有限公司 | Method and device for collecting multimedia resources |
Also Published As
| Publication number | Publication date |
|---|---|
| US20190394529A1 (en) | 2019-12-26 |
| US11153653B2 (en) | 2021-10-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109815368A (en) | Resource recommendation method, device, equipment and computer readable storage medium | |
| Ferraro et al. | What is fair? Exploring the artists’ perspective on the fairness of music streaming platforms | |
| US11455465B2 (en) | Book analysis and recommendation | |
| Chiny et al. | Netflix recommendation system based on TF-IDF and cosine similarity algorithms | |
| US8321203B2 (en) | Apparatus and method of generating information on relationship between characters in content | |
| US20190098371A1 (en) | Media narrative presentation systems and methods with interactive and autonomous content selection | |
| US20140108143A1 (en) | Social content distribution network | |
| CN109511015B (en) | Multimedia resource recommendation method, device, storage medium and equipment | |
| US8126763B2 (en) | Automatic generation of trailers containing product placements | |
| Tran et al. | Exploiting character networks for movie summarization | |
| Furini et al. | Sentiment analysis and twitter: a game proposal | |
| KR20120088650A (en) | Estimating and displaying social interest in time-based media | |
| US11586756B2 (en) | Article management system | |
| CN110474944B (en) | Network information processing method, device and storage medium | |
| CN112364202A (en) | Video recommendation method and device and electronic equipment | |
| CN114845149B (en) | Video clip method, video recommendation method, device, equipment and medium | |
| CN119066271B (en) | Intelligent recommendation method and system for educational books | |
| US20150112814A1 (en) | System and method for an integrated content publishing system | |
| US20140161423A1 (en) | Message composition of media portions in association with image content | |
| KR102439652B1 (en) | Method and system for providing online customized advertisements based on related posts written after the broadcasting time of broadcasting contents | |
| CN116010694A (en) | Search filter item display method, device, electronic device and storage medium | |
| US9740695B2 (en) | Method for enriching a multimedia content, and corresponding device | |
| US20140163956A1 (en) | Message composition of media portions in association with correlated text | |
| US20140089130A1 (en) | System and method for making gift recommendations using social media data | |
| Zhu et al. | Prediction of online topics’ popularity patterns |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20210518 Address after: 100085 Baidu Building, 10 Shangdi Tenth Street, Haidian District, Beijing Applicant after: BEIJING BAIDU NETCOM SCIENCE AND TECHNOLOGY Co.,Ltd. Applicant after: Shanghai Xiaodu Technology Co.,Ltd. Address before: 100085 Baidu Building, 10 Shangdi Tenth Street, Haidian District, Beijing Applicant before: BEIJING BAIDU NETCOM SCIENCE AND TECHNOLOGY Co.,Ltd. |
|
| TA01 | Transfer of patent application right | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190528 |
|
| RJ01 | Rejection of invention patent application after publication |